The landscape of business has changed drastically in a short period of time. With the internet becoming a facet of marketing and customer relations and with the rise of the Internet of Things connecting objects and technology, many businesses have naturally evolved into highly cyber entities.

One of the great benefits of these changes is a growing understanding and innovative use of big data. In essence, data is the oil that slicks a smooth running business. It provides transparency for marketing, product, and productivity, to name just a few.

With the increased usage of data also come new obstacles and challenges.

While solutions are evolving within the world of ever-advancing technology, they are based upon the need for safe storage, ease of access, and the ability to process and analyze from a raw data set.

Understanding the Terms 

What is a Data Lake?

Generally, data is useful when it has been processed. Yet, many organizations continually collect and store data over an extended period of time until it’s needed. This is where data lake’s come in.

Data lakes provide storage repository for raw, unprocessed data. One of the great benefits of data lakes is their flat architecture style of storage. Instead of using a hierarchical organization, such as data warehouses, data lakes use unique identifiers and metadata tags that can then be queried by using relevant and related keywords. This format provides overall search abilities instead of independent or select searches based upon the location of data.

What is Data Ingestion?

Data ingestion and data lakes go hand-in-hand. While data lakes store raw data, data ingestion is the act of locating, securing, and importing said raw data for direct use or storage.

Data ingestion includes to two types of downloads, either streamed real-time or imported batches. Per TechTarget.com, “when data is ingested in real time, each data item is imported as it is emitted by the source. When data is ingested in batches, data items are imported in discrete chunks at periodic intervals of time.”

Both types are effective, yet an efficient data ingestion system looks to prioritize so that data can be correctly organized for access and reference at any time.

The Beneficial Relationship: Big Data, Data Lakes, and Data Ingestion

When it comes to big data, data ingestion and data lakes are key components for optimizing information. The relationship is based upon qualified importing, high-level organization, and effective analysis. Yet, there are many additional benefits that can be gained.

  • Storage

The obvious benefit of data lake storage is their ability to efficiently and safely store large amounts of data or “big data”. Yet, it’s not as simple as that. While data lakes store large amounts of data, they are also able to become as large (or as small) as needed. On top of that, they provide this service at a reasonable cost.

  • Ingesting Data: Bulk and High Velocity 

Handling both bulk and high-velocity data is a feat for any storage program. Not only do data lakes require scalable tools that can efficiently import and organize batch and micro-batch data, but they must also be able to capture and queue data moving at high velocity.

This challenge has been met with an Apache Hadoop system. Generally, data lakes use a Hadoop architecture. Hadoop is a java-based program providing structure for systems to store large amounts of data. The architecture is designed to handle both bulk and high-velocity importing.

  • Structure, Process, and Integrate Data

In the end, it’s about utility.

Data that is effectively stored in its raw state will eventually need to be structured, processed, analyzed, and then integrated. While analysis can be outsourced to data scientists or data analysis teams, there is well-reviewed software available for businesses to do the work in-house. Data lakes make this process even simpler by integrating analytical and processing software such as GemFire, HAWQ, or Pivotal Greenplum.

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